Development of a biodegradable sirolimus-eluting stent coated by ultrasonic atomizing spray

In this study, poly(D,L lactic-co-glycolic acid) (PLGA) was used as a drug carrier to generate two types of stents loaded with different concentrations of sirolimus. These stents were prepared by ultrasonic atomizing spray coating. Ultrasonic atomizing spray nozzle uses a low-pressure air/gas to produce a soft, highly focused beam of small spray drops. An isolated hypotube delivers liquid to the nozzle's atomizing surface while air/gas, delivered through the nozzle orifice at a fixed low pressure, shapes the atomized drops into a very precise, targeted spray. The stent was moved both in the traverse direction and rotated during the spraying process. The morphology of the sirolimus-eluting stents was examined by scanning electron microscopy (SEM) which indicated that the coating was very smooth and uniform. The coating was found to have the ability to withstand the compressive and tensile strains imparted without cracking during the stent inflation process. Release profile of sirolimus was measured by high performance liquid chromatography (HPLC). The release behavior of sirolimus from the stent surface had a two phase release profile with a burst release period of about 2 days, followed by a sustained and slow release phase. The mass loss behavior of PLGA appeared linear throughout most of the degradation period. At 28 days, neointimal formation was found to be significantly decreased for both sirolimus-eluting stents as compared to bare-metal stents (BMS). Assessment of vascular healing revealed an absence of increased inflammation in both sirolimus-eluting stents. Inflammation is commonly observed in drug-eluting stents (DES) with nonbiodegradable polymeric coatings. Taking these results into account, these novel sirolimus-eluting stents may be good candidates to resolve in-stent restenosis.     

A RUNNER-GATE DESIGN SYSTEM FOR DIE CASTING

Various analytical finite element method (FEM) tools have been developed for flow processes, including die casting, but they only give information about whether the predetermined die design is correct. Current shop practice uses trial-and-error methods to determine new die designs. This article describes development of a computer-aided die design system for die casting. The computer-assisted design (CAD) system was written using Auto LISP with a personal computer. This system was developed to present algorithms for automation of die design, especially a runner-gate system using three-dimensional geometry. This system quantifies practical knowledge and experiences in die design as the formulating procedure. The system allows engineers to make automatic and efficient designs and it will result in reduction of required expenses and time. The system is composed of selection of cast alloy and product design, and uses the runner-gate design. In addition, specific rules and equations for the system are presented. An example is applied to a cap-shaped casting using the proposed system.     

Shape-dependent cytotoxicity of polyaniline nanomaterials in human fibroblast cells

The toxicity of polyaniline (PANI) nanomaterials with four different aspect ratios on human lung fibroblast cells was investigated by cell viability assay, cytotoxicity assay, apoptosis/necrosis measurement, and reactive oxygen species production. The toxicity increased with decreasing aspect ratio of PANI nanomaterials. In contrast, the highest aspect ratio PANI nanomaterials showed similar results with bulk PANI materials. The adverse effect of PANI nanomaterials was also concentration- and time-dependent. Low aspect ratio PANI nanomaterials induced more necrosis and more reactive oxygen species than others. These results provide new understanding of shape-dependent toxicity of nanomaterials.     

Analysis of Laminated and Sandwich Composite Structures Using Solid-like Shell Elements

A new solid-like shell element was formulated which is suitable for analysis of laminated and sandwich composite structures. Then, a multiscale analysis technique was implemented to the shell element formulation so that micro-level stresses and strains (i.e. stresses and strains in reinforcing fibers and the binding matrix) in those structures can be computed. The shell element has three displacement degrees of freedom per node like a 3-D solid element. Therefore, the shell elements can be stacked easily on top of one another like 3-D solid elements in order to represent multiple layers through the thickness of laminated and sandwich structures. The effect of a thin resin or adhesive layer in laminated and sandwich composite structures was investigated on both static and the dynamic responses of the structures using the developed shell elements. The study showed an apparent effect of the resin/adhesive layer even though it is very thin. As a result, the present shell element can be used effectively to include those thin layers in finite element analysis models of laminated and sandwich composite structures.     

Water-soluble MnO nanocolloid for a molecular T1 MR imaging: a facile one-pot synthesis, in vivo T1 MR images, and account for relaxivities

A facile one-pot synthesis of a water-soluble MnO nanocolloid (i.e., D-glucuronic acid-coated MnO nanoparticle) is presented. The MnO nanoparticle in the MnO nanocolloid was coated with a biocompatible and hydrophilic D-glucuronic acid, and its particle diameter was nearly monodisperse and ranged from 2 to 3 nm. The average hydrodynamic diameter of the MnO nanocolloid was estimated to be 5 nm. The MnO nanoparticle was nearly paramagnetic down to T=3 K. The MnO nanocolloid showed a high longitudinal water proton relaxivity of r1=7.02 s(-1) mM(-1) with the r2/r1 ratio of 6.83 due to five unpaired S-state electrons of Mn(II) ion (S=5/2) as well as a high surface to volume ratio of the MnO nanoparticle. High contrast in vivo T1 MR images were obtained for various organs, showing the capability of the MnO nanocolloid as a sensitive T1 MRI contrast agent. The suggested three key-parameters which control the r1 and r2 relaxivities of nanocolloids (i.e., the S value of a metal ion, the spin structure, and the surface to volume ratio of a nanoparticle) successfully accounted for the observed r1 and r2 relaxivities of the MnO nanocolloid.     

UV nano embossing for polymer nano structures with non-transparent mold insert

In recent years, UV nano embossing (or imprinting) process has been widely used for mass replication of nano structures. Most of the UV embossing machines use a UV transparent mold insert (e.g. quartz or glass). However, when a master of nano structures of interest could be realized only in a UV non-transparent mold material, it would be desirable to have a UV embossing machine which could be operated with such a UV non-transparent mold insert. In this regard, we have designed and manufactured a new UV embossing system in such a way that UV non-transparent (e.g., metal or ceramic) mold inserts can also be used as the master for the mass replication of nano structures. For the new UV embossing system, we fabricate several metal mold inserts: a nickel electroformed mold insert having grating nano structures and two AAO (anodic aluminum oxide) mold inserts having dimple nano structures and high-aspect-ratio nanopores. Finally, corresponding nano structures are successfully replicated via the UV nano embossing machine developed in this study.     

HDAC8-Selective Inhibition by PCI-34051 Enhances the Anticancer Effects of ACY-241 in Ovarian Cancer Cells

HDAC6 is overexpressed in ovarian cancer and is known to be correlated with tumorigenesis. Accordingly, ACY-241, a selective HDAC6 inhibitor, is currently under clinical trial and has been tested in combination with various drugs. HDAC8, another member of the HDAC family, has recently gained attention as a novel target for cancer therapy. Here, we evaluated the synergistic anticancer effects of PCI-34051 and ACY-241 in ovarian cancer. Among various ovarian cancer cells, PCI-34051 effectively suppresses cell proliferation in wild-type p53 ovarian cancer cells compared with mutant p53 ovarian cancer cells. In ovarian cancer cells harboring wild-type p53, PCI-34051 in combination with ACY-241 synergistically represses cell proliferation, enhances apoptosis, and suppresses cell migration. The expression of pro-apoptotic proteins is synergistically upregulated, whereas the expressions of anti-apoptotic proteins and metastasis-associated proteins are significantly downregulated in combination treatment. Furthermore, the level of acetyl-p53 at K381 is synergistically upregulated upon combination treatment. Overall, co-inhibition of HDAC6 and HDAC8 through selective inhibitors synergistically suppresses cancer cell proliferation and metastasis in p53 wild-type ovarian cancer cells. These results suggest a novel approach to treating ovarian cancer patients and the therapeutic potential in developing HDAC6/8 dual inhibitors.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.